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Data Science Seminar | Federated Learning: The future of Edge Intelligence is now!
In recent years, the ever-increasing resource capacities and allocated data at the network's edge encouraged shifting the data analytics to new generations of big data decentralised systems. Aiming at enabling on-device collaborative training of distributed machine learning and artificial intelligence models, edge intelligence came to realise.

Federated Learning (FL) emerged as a popular privacy-preserving approach across this realm, aiming at conducting inference when data are decentralised and locally stored on several distributed nodes, under two main constraints: data ownership and communication overhead.

This seminar will host four experts to provide insights on deploying a reliable and privacy-preserving edge intelligence solution from leading academic practitioners and industry partners. In particular, we will have two FL presentations, one presentation on the security of disconnecting IoT devices and one presentation on Graph federated data discovery and source.

➠ Peter Richtarik, Professor of Computer Science @ KAUST, Kingdom of Saudi Arabia
➠ Andreas Hellander, Associate Professor in Scientific Computing @ University of Uppsala, Sweden
➠ Aaron Ardiri, CEO & Founder @ RIoT Secure, Sweden
➠ Essam Mansour, Assistant Professor of Data Systems @ Concordia University, Canada

➠ Ahmed Awad, Professor of Data Systems @ University of Tartu, Estonia
➠ Feras M. Awaysheh, Assistant Professor of Data Analytics @ University of Tartu, Estonia

The seminar will be held in English and virtually in Zoom.

Participation is free of charge, but registration is needed to attend.

Nov 18, 2021 05:00 PM in Helsinki

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